Automated Real Time Wakefulness State Observation of Driver based on Machine Learning

Safe driving poses a serious problem for communities all around the world. Every year, thousands of people are killed or critically wounded as a result of drivers falling asleep behind the wheel. According to recent study, drowsy driving accelerates to 20% of catastrophic or fatal accidents on motorways or monotonous roads, weakening drivers' judgement and ability to operate automobiles. Studies also suggest that driving when fatigued increases the risk of death more than texting while driving, distracted driving and drunk driving combined altogether. The dangers of being drowsy on the road results, fatalities, severe injuries and significant economic losses. As a result, developing a real-time safety system for drowsiness-related traffic accident prevention is critical. The overall research findings which are based on a thorough survey, are drawn to conclusion and will aid researches in locating prospective future work in the relevant sector.